Deep learning. Topics such as identification and reasoning are much less studied in deep learning [1]}. Instead, the focus on deep learning is a much more pragmatic one. Deep learning research is concerned with increasing accuracy [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, improving quality of synthetic data [9]}, [10]}, [11]}, [12]}, or dealing with an increasing amount of parameters [13]}, [14]}, [15]}. These advances are completely ignored by Pearl's ladder (as it only focuses on the causal knowledge embedded in the models). This fact is illustrated by including some examples of models that are parameterised very differently (from linear regression [16]} to transformer networks [17]}.
